1. Field of the Invention
The present invention relates to an apparatus which discriminates the type of recording material used and an image formation apparatus for the same.
2. Description of the Related Art
Conventionally, an image formation apparatus such as copying machines or laser printers with an electrophotographic process is capable of automatically discriminating the type of recording materials used and accordingly modifying developing conditions, transfer conditions, and/or fixing conditions based on the type of the recording material that is discriminated.
There exists a method of automatically discriminating the type of recording material. The method comprises picking up images of the recording material surface by using a CCD sensor, converting the obtained surface image into fractal dimension data, evaluating the surface smoothness of the recording material, and then discriminating the type of recording material based on the surface smoothness. For example, Japanese Patent Application Laid-Open No. 2002-182518 discloses the following method. First, a surface image of the recording material is picked up by using a CCD or CMOS sensor. Since the recording material surface is uneven because of paper fibers or other elements, a tone distribution image of various light quantity, which varies according to the type of recording material, is obtained. Subsequently, the roughness of the recording material surface (surface smoothness) is obtained based on this tone distribution image, and the type of recording material is then discriminated based on the surface smoothness.
However, this method of discriminating the type of recording material based on the surface smoothness is deficient in some respects. For example, in the case where the surface smoothness for thick (i.e., heavy) paper sheets is the same as that for thinner plain paper sheets, the thick paper sheets will not be discriminated as such, and instead be mistaken for plain paper. For this reason, methods which detect the thickness of the recording material used for image forming, and then discriminate the type of recording material based on the surface smoothness and thickness thereof, have also been proposed as a means to solve this problem.
In the method used in the conventional image formation apparatus for discriminating the type of recording material based on the surface smoothness thereof, an image pickup sensor inevitably measures the light quantity unevenness due to the light source. In the conventional discrimination method, the parameter used to measure the surface smoothness of the recording material, the peak-to-peak value, which is the value of the difference between the highest and the lowest density values in the picked up image data, has been used as a parameter to discriminate the surface smoothness of the recording material. As a result, when measuring the recording material, the measured results of the surface smoothness would deviate from the true value due to light quantity unevenness. This deviation deteriorates the performance of discrimination the type of recording material. In order to circumvent this problem, the conventional art goes through the following processing steps. First, when measuring the recording material, images of the recording material are picked up multiple times while being moved. Next, the light quantity unevenness is obtained by averaging the imaging results. Namely, the process of shading is conducted based on the plurality of image data obtained from the multiple imaging to obtain the light quantity unevenness. Finally image data corresponding to the degree of the light quantity unevenness is calculated and removed from the image data picked up for final discrimination.
The conventional discrimination method will now be described in detail with reference to FIGS. 6, 8, and 9.
As shown in FIG. 6, the images 40, 41, and 42 are images of recording material surfaces, and by digitally processing these images, results like images 43, 44, and 45, for example, are obtained. When surface paper fibers are coarse, as is the case in recording material A, a large number of fiber shadows are created. Since the occurrence of these fiber shadows increases the difference between the bright and dark portions of the surface image, the peak-to-peak value becomes correspondingly large. Furthermore, for recording materials whose fibers have been substantially compressed and are thereby highly smooth, such as recording material C, the surface images thereof exhibit few fiber shadows and thus the peak-to-peak value is correspondingly small.
The peak-to-peak value derived from the Dmax and Dmin values can be calculated separately for each line (see image 43 in FIG. 6) in the digitally processed image data. In a first embodiment of the present invention, a single set of image data consists of data describing an 8×8 pixel array, and therefore peak-to-peak data for eight lines is obtained. Consequently, peak-to-peak values may be evaluated for a plurality of lines by the CMOS area sensor 801, and by averaging or integration of these values, a detection result for the whole of the imaged area of the recording material is obtained.
As described above, when the method of calculating peak-to-peak values for individual lines is employed, the light quantity from the light source might be uneven across a single line, and therefore the peak-to-peak values might be skewed as a result. FIG. 8 shows graphs illustrating this phenomenon. The graphs in FIG. 8 represent data taken from only one line of the two-dimensional output as measured by the CMOS area sensor 801. When the peak-to-peak values for plain paper and glossy paper are evaluated, although there is still a difference between these peak-to-peak values, the light quantity unevenness of the light source creates a slope (swell) in the data that is larger than the actual tone data for each pixel due to paper fibers. This slope in the data occurs as a result of the light quantity unevenness, and represents measurement errors.
Consequently, conventional methods like the above perform additional processing steps, including performing calibration processing by taking a plurality of images and then averaging each image data by each pixel in order to measure the light quantity unevenness therein. Subsequently, conventional methods perform image correction to remove the measured light quantity unevenness; in other words, shading correction has been performed. In addition, there is also an imaging method whereby instead of taking and measuring a plurality of images, the recording material is imaged during carrying, and per-pixel fiber coarseness is not measured. Shading correction will now be described with reference to FIG. 9. The uppermost graphs are schematic views of data taken from only one line of the two-dimensional output as measured by the CMOS area sensor 801. The light quantity data includes light quantity unevenness data as shown by the curves. By taking a plurality of such images and then averaging each pixel, the light quantity unevenness data (data for process of shading) that is close to the light quantity unevenness with the large slope (swell) in the graph is obtained. By subtracting this data from the actual obtained images, tone data (data corrected by shading correction) by the recording material's fiber structure, and from which the light quantity unevenness has been removed is obtained.
However, in the case where the process of shading is conducted in order to remove the effects at the sensor of the light quantity unevenness caused by the light source, the light quantity unevenness must be measured by imaging the recording material multiple times with the sensor while moving the recording material. As a result, a longer time is spent for imaging the recording material. If the time for imaging the recording material is long, the time required for discrimination of the type of recording material increases, and the start of the image forming process is delayed. Furthermore, the printing of the first page of the material to be completed is also delayed. This printing delay caused by the discrimination processing of the recording material not only delays the completion of printing the first page, it also potentially lowers the continuous printing speed. For example, in the case where the discrimination processing is conducted for each page in order to detect the type of recording material, the diminished printing speed is pronounced.
In addition, in the conventional discrimination method using peak-to-peak values, which is the value of the difference between the highest and the lowest density values in the picked-up image data, there are cases wherein the recording material cannot be sufficiently discriminated. For example, there exist some materials having a variety of surface conditions that are utilized by users as recording materials, and there are cases where discriminating these kinds of recording materials using the conventional method makes it very difficult to discriminate them as plain paper or rough paper.